no code implementations • 21 Jan 2024 • Cheng Li, Weijian Huang, Hao Yang, Jiarun Liu, Shanshan Wang
Particularly, raw radiology reports are refined to highlight the key information according to a constructed clinical dictionary and two model-optimized knowledge-enhancement metrics.
1 code implementation • 4 Jan 2024 • Hao Yang, Hong-Yu Zhou, Zhihuan Li, Yuanxu Gao, Cheng Li, Weijian Huang, Jiarun Liu, Hairong Zheng, Kang Zhang, Shanshan Wang
Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics.
no code implementations • 3 Jan 2024 • Jiarun Liu, Hong-Yu Zhou, Cheng Li, Weijian Huang, Hao Yang, Yong Liang, Shanshan Wang
Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs.
no code implementations • 3 Jan 2024 • Weijian Huang, Cheng Li, Hong-Yu Zhou, Jiarun Liu, Hao Yang, Yong Liang, Guangming Shi, Hairong Zheng, Shanshan Wang
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications.
no code implementations • 3 Jan 2024 • Hao Yang, Hong-Yu Zhou, Cheng Li, Weijian Huang, Jiarun Liu, Yong Liang, Shanshan Wang
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking.
no code implementations • 18 Dec 2023 • Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Shanshan Wang
The other 100 (8, 800 image slices) are for left atrium segmentation.
no code implementations • 12 Sep 2023 • Weijian Huang, Cheng Li, Hao Yang, Jiarun Liu, Shanshan Wang
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field.
no code implementations • 12 Apr 2023 • Hao Yang, Weijian Huang, Jiarun Liu, Cheng Li, Shanshan Wang
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.
no code implementations • 15 Mar 2023 • Weijian Huang, Hao Yang, Cheng Li, Mingtong Dai, Rui Yang, Shanshan Wang
To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports knowledge transformation.
no code implementations • 16 Nov 2022 • Cheng Li, Yousuf Babiker M. Osman, Weijian Huang, Zhenzhen Xue, Hua Han, Hairong Zheng, Shanshan Wang
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic.
no code implementations • 16 Nov 2022 • Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Nazik Elsayed, Zhenzhen Xue, Hairong Zheng, Shanshan Wang
The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
no code implementations • 15 Nov 2022 • Haoran Li, Cheng Li, Weijian Huang, Xiawu Zheng, Yan Xi, Shanshan Wang
In this work, we propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.
no code implementations • 15 Nov 2022 • Yeqi Wang, Weijian Huang, Cheng Li, Xiawu Zheng, Yusong Lin, Shanshan Wang
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic.
no code implementations • 18 Mar 2022 • Weijian Huang, Cheng Li, Wenxin Fan, Yongjin Zhou, Qiegen Liu, Hairong Zheng, Shanshan Wang
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
no code implementations • 5 Aug 2020 • Weijian Huang, Hao Yang, Xinfeng Liu, Cheng Li, Ian Zhang, Rongpin Wang, Hairong Zheng, Shan-Shan Wang
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning.
2 code implementations • 14 Aug 2019 • Yongjin Zhou, Weijian Huang, Pei Dong, Yong Xia, Shan-Shan Wang
This function adds a weighted focal coefficient and combines two traditional loss functions.
2 code implementations • 16 Jul 2019 • Hao Yang, Weijian Huang, Kehan Qi, Cheng Li, Xinfeng Liu, Meiyun Wang, Hairong Zheng, Shan-Shan Wang
To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.